Quality and Reliability Engineering International

Processing new types of quality data

Early View

Abstract Quality engineers are increasingly faced with the need to deal with new types of data, which are significantly different from ordinary numerical data by virtue of their nature and the operations that can be performed with them. Basic concepts related to processing of such data, ie, data similarity, measurement system analysis, variation analysis, and data fusion, need to be thoroughly rethought. Reviewing recent publications in the field, we suggest a common approach to processing all data types on the basis of the idea of defining the distance metric for the appropriate data space. The article discusses six types of quality data (nominal, ordinal, preference chains, strings, tree structured, and product/process distribution) and four data processing aspects (calculating data similarity, error description, data fusion, and intradispersion and interdispersion studies). Necessary information and recommendations are given for each combination of data type and problem. They are also summarized in a table that refers the reader to various sections of the article. Any other data type for which the distance metric is definable can be included into the framework of the proposed unified approach.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.